Platform Support Engineer

As a Platform Support Engineer, you'll be responsible for designing, deploying, and maintaining Kubernetes clusters on major cloud platforms like AWS, Azure, and GCP. You'll also manage cloud-native infrastructure components, automate processes using Terraform, and ensure consistent deployments with GitOps principles. Establishing monitoring systems and troubleshooting incidents will be essential parts of your role, all while collaborating with development teams and implementing security best practices.

To qualify as a Platform Support Engineer at our company, you should have at least 3 years of hands-on experience with Kubernetes in production settings. Strong knowledge of AWS, Azure, and GCP, along with core services and security practices, is crucial. You should also be familiar with GitOps workflows using ArgoCD, possess strong programming skills in languages like Python or Go, and have a good understanding of SRE practices.

A Platform Support Engineer should be proficient in at least one programming language, such as Python, Go, or Java, for automation and scripting tasks. These skills will be vital for developing scripts and tools to streamline workflows and reduce operational overhead, making you a key player in our engineering efforts.

Infrastructure as Code (IaC) and GitOps are critical methodologies in the role of a Platform Support Engineer. IaC, particularly through tools like Terraform, allows for automated provisioning and management of infrastructure, promoting consistency and reducing manual effort. GitOps utilizes version control systems for infrastructure changes, ensuring that deployments are reliable and that the actual state of your systems matches the desired state, which enhances overall stability and traceability.

As a Platform Support Engineer, you will typically work from 8:30 PM to 5:30 AM IST. This schedule is designed to provide coverage for US time zones, allowing you to collaborate effectively with teams across the globe while keeping our cloud infrastructure operating seamlessly.

In managing deployments on Kubernetes, I follow a structured approach that includes defining deployment configurations as code, leveraging CI/CD pipelines for automation, and utilizing tools like ArgoCD for GitOps principles. This ensures that deployments are not only consistent and reliable but also auditable and easy to revert if necessary.

To optimize Kubernetes cluster performance, I regularly monitor resource usage and conduct performance assessments to identify bottlenecks. Implementing resource quotas and limits, optimizing pod configurations, and utilizing Horizontal Pod Autoscaling are key aspects of my strategy. Additionally, I assess and refine network policies and storage configurations to ensure efficiency.

I employ tools such as Prometheus for metrics collection and Grafana for visualization to monitor the health of the Kubernetes clusters. For alerting, I configure Alertmanager to send notifications based on defined thresholds and incidents, ensuring a proactive approach to cloud environment management.

Ensuring security in Kubernetes environments involves implementing Role-Based Access Control (RBAC) to restrict access, using network policies to control traffic, and maintaining up-to-date container images to mitigate vulnerabilities. Regular security audits and adhering to compliance standards are also essential practices I follow to maintain security hygiene.

I have extensive experience developing automation scripts using Python and Go to streamline various processes, such as managing Kubernetes configurations, automating backups, and implementing deployment workflows. This not only reduces manual effort but also increases reliability and standardization across the platforms I manage.

One significant incident in a cloud environment involved a sudden performance degradation of a critical application running in Kubernetes. I quickly implemented a step-by-step troubleshooting process, analyzing logs, checking resource usage, and eventually identifying a misconfigured resource limit. By adjusting the settings, I restored performance and documented the incident for future learning.

I prioritize communication and collaboration with development teams to understand their requirements and challenges. Regular meetings, sharing best practices, and providing guidance on platform features are part of my approach. This collaboration ensures smooth integration of applications and fosters a culture of shared responsibility.

Service Level Objectives (SLOs) are critical indicators that define the expected performance and reliability of a service. They are important because they set clear expectations for service delivery, help teams prioritize incidents based on their impact, and provide measurable goals for continuous improvement of platform reliability.

I approach on-call responsibilities with a proactive mindset, ensuring I am well-prepared for potential incidents. I maintain detailed documentation and runbooks that outline troubleshooting steps for common issues. During incidents, I prioritize clear communication and collaboration with the team to resolve issues quickly while minimizing service disruption.

To manage costs in cloud environments, I regularly analyze resource usage and identify underutilized or over-provisioned resources. Implementing autoscaling solutions helps to adjust resources dynamically based on demand. Additionally, I often explore the use of spot instances for non-critical workloads and leverage cost management tools to gain visibility over spending patterns.